U.S.–China trade war and corporate reallocation:Evidence from Chinese listed companies

This paper applies a difference-in-differences framework to explore the economic consequences of the recent U.S.–China trade war. The average abnormal returns of Chinese listed firms during a period centered on President Trump's announcement on 22 March 2018 are taken as a proxy for the firms' exposure to the potential trade war. Firms more negatively exposed are found, surprisingly, to report higher total revenues in the post-announcement period. The results indicate that the Chinese firms tend to reallocate their business from overseas to the domestic market. Such within-firm reallocation is found to be more pronounced among private firms, exporting firms and non-FDI firms. Besides, firms with higher negative exposure increase total investment and financing but decrease foreign investment after the trade war

Triplet Attention Transformer for Spatiotemporal Predictive Learning

Spatiotemporal predictive learning offers a self-supervised learning paradigm that enables models to learn both spatial and temporal patterns by predicting future sequences based on historical sequences. Mainstream methods are dominated by recurrent units, yet they are limited by their lack of parallelization and often underperform in real-world scenarios. To improve prediction quality while maintaining computational efficiency, we propose an innovative triplet attention transformer designed to capture both inter-frame dynamics and intra-frame static features. Specifically, the model incorporates the Triplet Attention Module (TAM), which replaces traditional recurrent units by exploring self-attention mechanisms in temporal, spatial, and channel dimensions. In this configuration: (i) temporal tokens contain abstract representations of inter-frame, facilitating the capture of inherent temporal dependencies; (ii) spatial and channel attention combine to refine the intra-frame representation by performing fine-grained interactions across spatial and channel dimensions. Alternating temporal, spatial, and channel-level attention allows our approach to learn more complex short- and long-range spatiotemporal dependencies. Extensive experiments demonstrate performance surpassing existing recurrent-based and recurrent-free methods, achieving state-of-the-art under multi-scenario examination including moving object trajectory prediction, traffic flow prediction, driving scene prediction, and human motion capture.Comment: Accepted to WACV 202

Living supramolecular polymerization of fluorinated cyclohexanes

The development of powerful methods for living covalent polymerization has been a key driver of progress in organic materials science. While there have been remarkable reports on living supramolecular polymerization recently, the scope of monomers is still narrow and a simple solution to the problem is elusive. Here we report a minimalistic molecular platform for living supramolecular polymerization that is based on the unique structure of all-cis 1,2,3,4,5,6-hexafluorocyclohexane, the most polar aliphatic compound reported to date. We use this large dipole moment (6.2 Debye) not only to thermodynamically drive the self-assembly of supramolecular polymers, but also to generate kinetically trapped monomeric states. Upon addition of well-defined seeds, we observed that the dormant monomers engage in a kinetically controlled supramolecular polymerization. The obtained nanofibers have an unusual double helical structure and their length can be controlled by the ratio between seeds and monomers. The successful preparation of supramolecular block copolymers demonstrates the versatility of the approach

Robust Representation Learning for Unified Online Top-K Recommendation

In large-scale industrial e-commerce, the efficiency of an online recommendation system is crucial in delivering highly relevant item/content advertising that caters to diverse business scenarios. However, most existing studies focus solely on item advertising, neglecting the significance of content advertising. This oversight results in inconsistencies within the multi-entity structure and unfair retrieval. Furthermore, the challenge of retrieving top-k advertisements from multi-entity advertisements across different domains adds to the complexity. Recent research proves that user-entity behaviors within different domains exhibit characteristics of differentiation and homogeneity. Therefore, the multi-domain matching models typically rely on the hybrid-experts framework with domain-invariant and domain-specific representations. Unfortunately, most approaches primarily focus on optimizing the combination mode of different experts, failing to address the inherent difficulty in optimizing the expert modules themselves. The existence of redundant information across different domains introduces interference and competition among experts, while the distinct learning objectives of each domain lead to varying optimization challenges among experts. To tackle these issues, we propose robust representation learning for the unified online top-k recommendation. Our approach constructs unified modeling in entity space to ensure data fairness. The robust representation learning employs domain adversarial learning and multi-view wasserstein distribution learning to learn robust representations. Moreover, the proposed method balances conflicting objectives through the homoscedastic uncertainty weights and orthogonality constraints. Various experiments validate the effectiveness and rationality of our proposed method, which has been successfully deployed online to serve real business scenarios.Comment: 14 pages, 6 figures, submitted to ICD

Wafer-sized multifunctional polyimine-based two-dimensional conjugated polymers with high mechanical stiffness

One of the key challenges in two-dimensional (2D) materials is to go beyond graphene, a prototype 2D polymer (2DP), and to synthesize its organic analogues with structural control at the atomic- or molecular-level. Here we show the successful preparation of porphyrin-containing monolayer and multilayer 2DPs through Schiff-base polycondensation reaction at an air-water and liquid-liquid interface, respectively. Both the monolayer and multilayer 2DPs have crystalline structures as indicated by selected area electron diffraction. The monolayer 2DP has a thickness of∼0.7 nm with a lateral size of 4-inch wafer, and it has a Young's modulus of 267±30 GPa. Notably, the monolayer 2DP functions as an active semiconducting layer in a thin film transistor, while the multilayer 2DP from cobalt-porphyrin monomer efficiently catalyses hydrogen generation from water. This work presents an advance in the synthesis of novel 2D materials for electronics and energy-related applications

Understanding the Electron Beam Resilience of Two-Dimensional Conjugated Metal–Organic Frameworks

Knowledge of the atomic structure of layer-stacked two-dimensional conjugated metal–organic frameworks (2D c-MOFs) is an essential prerequisite for establishing their structure–property correlation. For this, atomic resolution imaging is often the method of choice. In this paper, we gain a better understanding of the main properties contributing to the electron beam resilience and the achievable resolution in the high-resolution TEM images of 2D c-MOFs, which include chemical composition, density, and conductivity of the c-MOF structures. As a result, sub-angstrom resolution of 0.95 Å has been achieved for the most stable 2D c-MOF of the considered structures, Cu3(BHT) (BHT = benzenehexathiol), at an accelerating voltage of 80 kV in a spherical and chromatic aberration-corrected TEM. Complex damage mechanisms induced in Cu3(BHT) by the elastic interactions with the e-beam have been explained using detailed ab initio molecular dynamics calculations. Experimental and calculated knock-on damage thresholds are in good agreement

An Ultra-fast Quantum Random Number Generation Scheme Based on Laser Phase Noise

Based on the intrinsic random property of quantum mechanics, quantum random number generators allow for access of truly unpredictable random sequence and are now heading towards high performance and small miniaturization, among which a popular scheme is based on the laser phase noise. However, this scheme is generally limited in speed and implementation complexity, especially for chip integration. In this work, a general physical model based on wiener process for such schemes is introduced, which provides an approach to clearly explain the limitation on the generation rate and comprehensively optimize the system performance. We present an insight to exploit the potential bandwidth of the quantum entropy source that contains plentiful quantum randomness with a simple spectral filtering method and experimentally boost the bandwidth of the corresponding quantum entropy source to 20 GHz, based on which an ultra-fast generation rate of 218 Gbps is demonstrated, setting a new record for laser phase noise based schemes by one order of magnitude. Our proposal significantly enhances the ceiling speed of such schemes without requiring extra complex hardware, thus effectively benefits the corresponding chip integration with high performance and low implementation cost, which paves the way for its large-scale applications.Comment: 25 pages, 7 figure

On-water surface synthesis of charged two-dimensional polymer single crystals via the irreversible Katritzky reaction

Two-dimensional polymers (2DPs) and their layer-stacked 2D covalent organic frameworks (2D COFs) are classes of structurally defined crystalline polymeric materials with exotic physical and chemical properties. Yet, synthesizing 2DP and 2D COF single crystals via irreversible reactions remains challenging. Here we report the synthesis of charged 2DP (C2DP) single crystals through an irreversible Katritzky reaction, under pH control, on a water surface. The periodically ordered 2DPs comprise aromatic pyridinium cations and counter BF4− anions. The C2DP crystals, which are composed of linked porphyrin and pyrylium monomers (C2DP-Por), have a tunable thickness of 2–30 nm and a lateral domain size up to 120 μm2. Single crystals with a square lattice (a = b = 30.5 Å) are resolved by imaging and diffraction methods with near-atomic precision. Furthermore, the integration of C2DP-Por crystals in an osmotic power generator device shows an excellent chloride ion selectivity with a coefficient value reaching ~0.9 and an output power density of 4 W m−2, superior to those of graphene and boron nitride

A Two-Dimensional Polyimide-Graphene Heterostructure with Ultra-fast Interlayer Charge Transfer

Two-dimensional polymers (2DPs) are a class of atomically/molecularly thin crystalline organic 2D materials. They are intriguing candidates for the development of unprecedented organic–inorganic 2D van der Waals heterostructures (vdWHs) with exotic physicochemical properties. In this work, we demonstrate the on-water surface synthesis of large-area (cm2), monolayer 2D polyimide (2DPI) with 3.1-nm lattice. Such 2DPI comprises metal-free porphyrin and perylene units linked by imide bonds. We further achieve a scalable synthesis of 2DPI-graphene (2DPI-G) vdWHs via a face-to-face co-assembly of graphene and 2DPI on the water surface. Remarkably, femtosecond transient absorption spectroscopy reveals an ultra-fast interlayer charge transfer (ca. 60 fs) in the resultant 2DPI-G vdWH upon protonation by acid, which is equivalent to that of the fastest reports among inorganic 2D vdWHs. Such large interlayer electronic coupling is ascribed to the interlayer cation–π interaction between 2DP and graphene. © 2021 The Authors. Angewandte Chemie International Edition published by Wiley-VCH Gmb